MATH 473 Machine Learning I
This course is designed to give students an introduction to statistical learning methods. Supervised learning topics include linear methods for regression and classification, support vector machines, decision trees, nearest-neighbors, basis function expansions, regularization, and ensemble methods. Unsupervised learning topics include clustering algorithms, principal component analysis, and dimensionality reduction. Emphasis is placed on practical implementation of machine learning algorithms using software.